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This is a tool intended to monitor the GPU usage on the various GPU-servers at the LIP6 Lab, Paris

A decentralized learning research framework

Cross domain dynamic

Test de protocol de consensus c4m

A blockgraph for mesh networks

ATEM is a novel framework for studying topic evolution in scientific archives. ATEM is based on dynamic topic modeling and dynamic graph embedding techniques that explore the dynamics of content and citations of documents within a scientific corpus. ATEM explores a new notion of contextual emergence for the discovery of emerging interdisciplinary research topics based on the dynamics of citation links in topic clusters. Our experiments show that ATEM can efficiently detect emerging cross-disciplinary topics within the DBLP archive of over five million computer science articles. paper: https://arxiv.org/abs/2306.02221

Implementing deep reinforcement learning algorithms for the ICGA competition.